As a result of continuous development, control and configuration algorithms are becoming very extensive and complex. Consequently, electronic devices and in particular mobile terminals must offer extensive resources to process said algorithms. In conventional methods, mobile terminals are therefore equipped with large data memories. This typically signifies greater expense with regard to the incorporation of device resources into the electronic device, since these device resources have an increased space requirement. Therefore the memory requirement in e.g. conventional methods can no longer be satisfied by a single memory element, and a multiplicity of memory elements must be provided instead, both for storing the control and configuration data and for processing the control and configuration algorithms.
High-speed processors are typically required for processing the extensive data records. These high-speed processors produce more heat, and therefore passive or active cooling elements are required when executing conventional methods. High-speed processors and a multiplicity of data memories result in an increased energy requirement of the electronic device. This results in shortened usability, particularly in the case of mobile terminals, since energy stores only allow a very limited operating period of the multiplicity of device resources.
In both mobile and stationary application scenarios in which searching for query data elements takes place, a plurality of computer systems interact via a radio interface. For example, a server provides a query data element to a client, wherein the search for the query element is implemented on the client on the basis of a data record. The provision of the query data element and a consequential provision of return parameters take place by means of a radio interface, for example. This results in a significant volume of data, which must be transmitted via e.g. a WLAN network. In this case, there is a possibility of data loss and/or unauthorized monitoring of the data that is transmitted. Furthermore, provision must be made for efficient network devices and large data memories.
In order to optimize a memory requirement, compression algorithms are used in conventional methods. In this case, the intention is to reduce memory requirement by encoding the data records in a suitable manner. These compression algorithms can incur data losses or result in zero losses depending on the application scenario. If an encoding does incur losses, a data loss up to a specific degree is accepted if this data loss appears to be justified by a reduced memory requirement. The data records are compressed on the sender side and decompressed on the receiver side. System resources for processing the compression or decompression algorithms are required in each case for this purpose.
Format-specific encoding specifications, which use e.g. a binary encoding for XML documents, are also known from conventional methods. The Efficient XML Interchange format (EXI) is one such example.
In conventional methods, the processing of extensive configuration and control algorithms gives rise to a greater resource requirement and hence implicitly to a greater energy requirement and delayed response times, in particular as a result of processing compression algorithms and decompression algorithms. In addition, data records that are provided in this context are typically accessed more frequently than is necessary for the purpose of searching efficiently for a query data element.